1. Field of the Invention
This invention generally relates to computer-implemented methods for performing one or more defect-related functions. Certain embodiments relate to computer-implemented methods for identifying noise in inspection data, binning defects, selecting defects for defect analysis, selecting one or more parameters of a defect review process, or classifying defects.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Inspection processes are used at various times during a semiconductor manufacturing process to detect defects on a specimen such as a reticle and a wafer. Inspection processes have always been an important part of fabricating semiconductor devices such as integrated circuits. However, as the dimensions of semiconductor devices decrease, inspection processes become even more important to the successful manufacture of acceptable semiconductor devices. For instance, as the dimensions of semiconductor devices decrease, detection of defects of decreasing size has become necessary since even relatively small defects may cause unwanted aberrations in the semiconductor devices.
Inspection for many different types of defects has also become more important recently. For instance, in order to use the inspection results to monitor and correct semiconductor fabrication processes, it is often necessary to know what types of defects are present on a specimen. In addition, since controlling every process involved in semiconductor manufacturing is desirable to attain the highest yield possible, it is desirable to have the capability to detect the different types of defects that may result from many different semiconductor processes. The different types of defects that are to be detected may vary dramatically in their characteristics. For example, defects that may be desirable to detect during a semiconductor manufacturing process may include thickness variations, particulate defects, scratches, pattern defects such as missing pattern features or incorrectly sized pattern features, and many others having such disparate characteristics.
In order for inspection to provide useful results for yield control, the inspection process must be able to not only detect many different kinds of defects but also to discriminate between real defects on the wafer or reticle and noise or nuisance events. Noise may be defined as events detected on a wafer or reticle by an inspection tool that are not actually defects but appear as potential defects due to marginalities in the inspection tool such as marginalities in data processing and/or data acquisition. Nuisance events are actual defects but that are not relevant to the user for the purposes of controlling the process or predicting yield. Moreover, the same defect may be considered as a nuisance event at one point in time, but it may later be found to be a relevant defect. In some instances, the number of noise and nuisance events detected by an inspection tool can be reduced by using optimized data acquisition parameters and optimized data processing parameters. In addition, the number of noise and nuisance events can be reduced by applying various filtering techniques to the inspection results.
One problem associated with noise and nuisance event reduction by the above method is the difficult and time consuming nature of determining the data acquisition and data processing parameters that will minimize noise and nuisance events. In particular, determining the appropriate data acquisition and data processing parameters typically involves a significant amount of time. In addition, the task of setting up an inspection process for a particular specimen and a specific defect type of interest may be particularly difficult when an inspection system has a relatively large number of adjustable data acquisition and data processing parameters. Furthermore, it may be impossible to know whether the best inspection process has been found unless all possible combinations of the data acquisition and data processing parameters have been tested.
However, most inspection processes are currently set up using a large number of manual processes (e.g., manually setting the data acquisition parameters, manually analyzing the resulting inspection data, etc.). As such, setting up the inspection process may take a relatively long time. Furthermore, depending on the types of specimens that will be inspected with the inspection system, a different inspection process may need to be set up for each different type of defect. The length of time involved in determining appropriate data acquisition and data processing parameters may be particularly problematic for cases such as a short experiment in development or a ramp for a short device-run in a foundry since these inspection processes do not provide a good return on the effort involved in setting them up.
Another important part of yield control is determining the cause of the defects on the wafer or reticle such that the cause of the defects can be corrected to thereby reduce the number of defects on other wafers or reticles. Often, determining the cause of the defects involves identifying the defect type and other characteristics of the defects such as size, shape, composition, etc. Since inspection typically only involves detecting defects on the wafer or reticle and providing limited information about the defects such as location, number, and sometimes size, defect review is often used to determine more information about individual defects than that which can be determined from inspection results. For instance, a defect review tool may be used to revisit defects detected on a wafer or reticle and to examine the defects further in some manner either automatically or manually. Defect review can also be used to verify that defects detected by inspection are actual defects instead of, for example, noise and nuisance events.
Some examples of commonly used defect review tools include high resolution optical imaging systems, scanning electron microscopes and less commonly transmission electron microscopes. Often, the tools used for defect review have a much lower throughput than inspection tools and can have negative effects on the material. Therefore, judicious use of the defect review tool is essential to provide sufficient information about defects on a reticle or wafer without significantly affecting the overall throughput of the process. One way in which the effect of defect review on the overall process throughput is mitigated is by reviewing only a subset or a portion of all of the defects that were detected by inspection.
Selecting defects for review is often called “sampling.” Although sampling defects for review is one primary way to improve the throughput of defect review, there are many ways in which sampling can adversely affect the information about defects generated by review. For example, one common way to sample defects for review is by random sampling of the entire defect population detected by inspection. In some instances, random sampling is effective to select a suitable defect subpopulation for review. However, there are many situations in which a random sample may not be desirable. For instance, real defects may be positioned randomly on a wafer that the user wants to be in the subpopulation for review for trending purposes, but these defects may dominate the population. Therefore, too few of the other types of defects may appear in a random sample to provide a defect subpopulation that is effective for review of all defect types of the wafer.
The effectiveness of the defect review process is also determined by the data acquisition parameters and/or data processing parameters used. In particular, much like inspection processes as described above, the data acquisition parameters and data processing parameters of a defect review process will have a profound effect on the defect review results. Therefore, it is important that defect review processes be performed with parameters that are suitable for the types of defects being reviewed. Like determining the appropriate parameters for inspection, determining the appropriate parameters for review can be relatively time consuming and difficult particularly when the defect review tool has a large number of adjustable parameters and/or a large number of different defects are to be reviewed in a single process. As such, it is conceivable that many defect review processes are currently being performed with parameters that are substantially less than optimal for at least some of the defects being reviewed.
Accordingly, it may be advantageous to develop computer-implemented methods for identifying noise in inspection data, binning defects, selecting defects for defect analysis, selecting one or more parameters of a defect review process, and/or classifying defects that provide higher throughput and better performance than currently available methods.